28 June 2006

From wikipedia: Genetic programming (GP) is an automated methodology inspired by biological evolution to find computer programs that best perform a user-defined task. It is therefore a particular machine learning technique that uses an evolutionary algorithm to optimize a population of computer programs according to a fitness landscape determined by a program's ability to perform a given computational task.. I wrote this applet when I wondered if I could use this (the answer was no:-)) to discover a mathematical way how to describe a biological signal.

The java program takes as input a table of result and a list of expected numbers. Top-Left: table of data used for input. Last columns are: expected result, normalized expected result, normalized computed result, fitness (|computed-expected|). Top-Right: nine steps of the evolution process. In each setp, on the X axis are displayed the expected values and the comuted values(calculated from the evolving methematical function) are on the Y axis. The fitness is diplayed. Bottom-Left: parameters, 'weight' of each function. Bottom-Right: The definition of the current best curve.

Hi! I am currently writing a (free and open) e-book called "Global Optimization - Theory and Application". It is about genetic algorithms, evolutionary algorithms, evolution strategy, leaning classifier systems, simulated annealing, and so on. I hope that I can make this topic more interesting for students and fellow researchers and help people to get started with it. Of course, the book is still very incomplete and probably has also errors, but I try to improve it very much. You can find it at http://www.it-weise.de/projects/book.pdf, I will update it regularly. Constructive criticism is always welcom, as well as new suggestions on what to include in the book.Kind regards,Thomas.